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线性和非线性泊松-玻尔兹曼方程的MPI-CUDA联合并行解决方案。

A combined MPI-CUDA parallel solution of linear and nonlinear Poisson-Boltzmann equation.

作者信息

Colmenares José, Galizia Antonella, Ortiz Jesús, Clematis Andrea, Rocchia Walter

机构信息

Drug Discovery and Development, Italian Institute of Technology, 16163 Genova, Italy ; Department of Geosciences, University of Padova, 35131 Padova, Italy.

IMATI, CNR, 16149 Genova, Italy.

出版信息

Biomed Res Int. 2014;2014:560987. doi: 10.1155/2014/560987. Epub 2014 Jun 12.

Abstract

The Poisson-Boltzmann equation models the electrostatic potential generated by fixed charges on a polarizable solute immersed in an ionic solution. This approach is often used in computational structural biology to estimate the electrostatic energetic component of the assembly of molecular biological systems. In the last decades, the amount of data concerning proteins and other biological macromolecules has remarkably increased. To fruitfully exploit these data, a huge computational power is needed as well as software tools capable of exploiting it. It is therefore necessary to move towards high performance computing and to develop proper parallel implementations of already existing and of novel algorithms. Nowadays, workstations can provide an amazing computational power: up to 10 TFLOPS on a single machine equipped with multiple CPUs and accelerators such as Intel Xeon Phi or GPU devices. The actual obstacle to the full exploitation of modern heterogeneous resources is efficient parallel coding and porting of software on such architectures. In this paper, we propose the implementation of a full Poisson-Boltzmann solver based on a finite-difference scheme using different and combined parallel schemes and in particular a mixed MPI-CUDA implementation. Results show great speedups when using the two schemes, achieving an 18.9x speedup using three GPUs.

摘要

泊松-玻尔兹曼方程对浸没在离子溶液中的可极化溶质上的固定电荷所产生的静电势进行建模。这种方法在计算结构生物学中经常用于估计分子生物学系统组装的静电能量成分。在过去几十年中,有关蛋白质和其他生物大分子的数据量显著增加。为了有效地利用这些数据,需要巨大的计算能力以及能够利用它的软件工具。因此,有必要朝着高性能计算发展,并开发现有算法和新算法的适当并行实现。如今,工作站可以提供惊人的计算能力:在配备多个CPU和诸如英特尔至强融核或GPU设备等加速器的单台机器上可达10万亿次浮点运算。充分利用现代异构资源的实际障碍是在这样的架构上进行高效的并行编码和软件移植。在本文中,我们提出基于有限差分格式实现一个完整的泊松-玻尔兹曼求解器,使用不同的和组合的并行方案,特别是混合MPI-CUDA实现。结果表明,使用这两种方案时加速比显著,使用三个GPU时加速比达到18.9倍。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/821c/4074970/8b30c69e5c73/BMRI2014-560987.001.jpg

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